Initial work for AIDA-X module
Signed-off-by: falkTX <falktx@falktx.com>
This commit is contained in:
falkTX
parent
09ad217134
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15 changed files with 1922 additions and 0 deletions
3
.gitmodules
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@ -227,3 +227,6 @@
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[submodule "plugins/Sapphire"]
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path = plugins/Sapphire
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url = https://github.com/cosinekitty/sapphire.git
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[submodule "plugins/Cardinal/src/AIDA-X/RTNeural"]
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path = plugins/Cardinal/src/AIDA-X/RTNeural
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url = https://github.com/jatinchowdhury18/RTNeural.git
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After Width: | Height: | Size: 3.4 KiB |
605
plugins/Cardinal/src/AIDA-X.cpp
Normal file
605
plugins/Cardinal/src/AIDA-X.cpp
Normal file
|
|
@ -0,0 +1,605 @@
|
|||
/*
|
||||
* AIDA-X Cardinal plugin
|
||||
* Copyright (C) 2022-2023 Massimo Pennazio <maxipenna@libero.it>
|
||||
* Copyright (C) 2023 Filipe Coelho <falktx@falktx.com>
|
||||
* SPDX-License-Identifier: GPL-3.0-or-later
|
||||
*/
|
||||
|
||||
#include "plugincontext.hpp"
|
||||
#include "ModuleWidgets.hpp"
|
||||
|
||||
#include "extra/Sleep.hpp"
|
||||
|
||||
#include "AIDA-X/Biquad.cpp"
|
||||
#include "AIDA-X/model_variant.hpp"
|
||||
|
||||
#ifndef HEADLESS
|
||||
# include "ImGuiWidget.hpp"
|
||||
# include "ghc/filesystem.hpp"
|
||||
#endif
|
||||
|
||||
// --------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
/* Define a constexpr for converting a gain in dB to a coefficient */
|
||||
static constexpr float DB_CO(const float g) { return g > -90.f ? std::pow(10.f, g * 0.05f) : 0.f; }
|
||||
|
||||
/* Define a macro to re-maps a number from one range to another */
|
||||
static constexpr float MAP(const float x, const float in_min, const float in_max, const float out_min, const float out_max)
|
||||
{
|
||||
return ((x - in_min) * (out_max - out_min) / (in_max - in_min)) + out_min;
|
||||
}
|
||||
|
||||
/* Defines for tone controls */
|
||||
static constexpr const float COMMON_Q = 0.707f;
|
||||
|
||||
/* Defines for antialiasing filter */
|
||||
static constexpr const float INLPF_MAX_CO = 0.99f * 0.5f; /* coeff * ((samplerate / 2) / samplerate) */
|
||||
static constexpr const float INLPF_MIN_CO = 0.25f * 0.5f; /* coeff * ((samplerate / 2) / samplerate) */
|
||||
|
||||
// --------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
struct DynamicModel {
|
||||
ModelVariantType variant;
|
||||
bool input_skip; /* Means the model has been trained with first input element skipped to the output */
|
||||
float input_gain;
|
||||
float output_gain;
|
||||
};
|
||||
|
||||
// --------------------------------------------------------------------------------------------------------------------
|
||||
// This function carries model calculations
|
||||
|
||||
static inline
|
||||
void applyModel(DynamicModel* model, float* const out, uint32_t numSamples)
|
||||
{
|
||||
const bool input_skip = model->input_skip;
|
||||
const float input_gain = model->input_gain;
|
||||
const float output_gain = model->output_gain;
|
||||
|
||||
std::visit(
|
||||
[&out, numSamples, input_skip, input_gain, output_gain] (auto&& custom_model)
|
||||
{
|
||||
using ModelType = std::decay_t<decltype (custom_model)>;
|
||||
|
||||
if (d_isNotEqual(input_gain, 1.f))
|
||||
{
|
||||
for (uint32_t i=0; i<numSamples; ++i)
|
||||
out[i] *= input_gain;
|
||||
}
|
||||
|
||||
if constexpr (ModelType::input_size == 1)
|
||||
{
|
||||
if (input_skip)
|
||||
{
|
||||
for (uint32_t i=0; i<numSamples; ++i)
|
||||
out[i] += custom_model.forward(out + i);
|
||||
}
|
||||
else
|
||||
{
|
||||
for (uint32_t i=0; i<numSamples; ++i)
|
||||
out[i] = custom_model.forward(out + i) * output_gain;
|
||||
}
|
||||
}
|
||||
|
||||
if (input_skip && d_isNotEqual(output_gain, 1.f))
|
||||
{
|
||||
for (uint32_t i=0; i<numSamples; ++i)
|
||||
out[i] *= output_gain;
|
||||
}
|
||||
},
|
||||
model->variant
|
||||
);
|
||||
}
|
||||
|
||||
static inline
|
||||
float applyModel(DynamicModel* model, float sample)
|
||||
{
|
||||
const bool input_skip = model->input_skip;
|
||||
const float input_gain = model->input_gain;
|
||||
const float output_gain = model->output_gain;
|
||||
|
||||
sample *= input_gain;
|
||||
|
||||
std::visit(
|
||||
[&sample, input_skip, output_gain] (auto&& custom_model)
|
||||
{
|
||||
using ModelType = std::decay_t<decltype (custom_model)>;
|
||||
float* out = &sample;
|
||||
|
||||
if constexpr (ModelType::input_size == 1)
|
||||
{
|
||||
if (input_skip)
|
||||
{
|
||||
sample += custom_model.forward(out);
|
||||
sample *= output_gain;
|
||||
}
|
||||
else
|
||||
{
|
||||
sample = custom_model.forward(out) * output_gain;
|
||||
}
|
||||
}
|
||||
},
|
||||
model->variant
|
||||
);
|
||||
|
||||
return sample;
|
||||
}
|
||||
|
||||
// --------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
struct AidaPluginModule : Module {
|
||||
enum ParamIds {
|
||||
PARAM_INPUT_LEVEL,
|
||||
PARAM_OUTPUT_LEVEL,
|
||||
NUM_PARAMS
|
||||
};
|
||||
enum InputIds {
|
||||
AUDIO_INPUT,
|
||||
NUM_INPUTS
|
||||
};
|
||||
enum OutputIds {
|
||||
AUDIO_OUTPUT,
|
||||
NUM_OUTPUTS
|
||||
};
|
||||
enum LightIds {
|
||||
NUM_LIGHTS
|
||||
};
|
||||
|
||||
enum Parameters {
|
||||
kParameterCount
|
||||
};
|
||||
|
||||
CardinalPluginContext* const pcontext;
|
||||
bool fileChanged = false;
|
||||
std::string currentFile;
|
||||
|
||||
Biquad dc_blocker { bq_type_highpass, 0.5f, COMMON_Q, 0.0f };
|
||||
Biquad in_lpf { bq_type_lowpass, 0.5f, COMMON_Q, 0.0f };
|
||||
dsp::ExponentialFilter inlevel;
|
||||
dsp::ExponentialFilter outlevel;
|
||||
DynamicModel* model = nullptr;
|
||||
std::atomic<bool> activeModel { false };
|
||||
|
||||
AidaPluginModule()
|
||||
: pcontext(static_cast<CardinalPluginContext*>(APP))
|
||||
{
|
||||
config(NUM_PARAMS, NUM_INPUTS, NUM_OUTPUTS, NUM_LIGHTS);
|
||||
|
||||
configInput(AUDIO_INPUT, "Audio");
|
||||
configOutput(AUDIO_OUTPUT, "Audio");
|
||||
configParam(PARAM_INPUT_LEVEL, -12.f, 12.f, 0.f, "Input level", " dB");
|
||||
configParam(PARAM_OUTPUT_LEVEL, -12.f, 12.f, 0.f, "Output level", " dB");
|
||||
|
||||
inlevel.setTau(1 / 30.f);
|
||||
outlevel.setTau(1 / 30.f);
|
||||
}
|
||||
|
||||
~AidaPluginModule() override
|
||||
{
|
||||
delete model;
|
||||
}
|
||||
|
||||
json_t* dataToJson() override
|
||||
{
|
||||
json_t* const rootJ = json_object();
|
||||
DISTRHO_SAFE_ASSERT_RETURN(rootJ != nullptr, nullptr);
|
||||
|
||||
json_object_set_new(rootJ, "filepath", json_string(currentFile.c_str()));
|
||||
|
||||
return rootJ;
|
||||
}
|
||||
|
||||
void dataFromJson(json_t* const rootJ) override
|
||||
{
|
||||
fileChanged = false;
|
||||
|
||||
if (json_t* const filepathJ = json_object_get(rootJ, "filepath"))
|
||||
{
|
||||
const char* const filepath = json_string_value(filepathJ);
|
||||
|
||||
if (filepath[0] != '\0')
|
||||
{
|
||||
currentFile = filepath;
|
||||
fileChanged = true;
|
||||
|
||||
loadModelFromFile(filepath);
|
||||
}
|
||||
}
|
||||
|
||||
if (! fileChanged)
|
||||
{
|
||||
currentFile.clear();
|
||||
fileChanged = true;
|
||||
}
|
||||
}
|
||||
|
||||
void loadModelFromFile(const char* const filename)
|
||||
{
|
||||
try {
|
||||
std::ifstream jsonStream(filename, std::ifstream::binary);
|
||||
loadModelFromStream(jsonStream);
|
||||
}
|
||||
catch (const std::exception& e) {
|
||||
d_stderr2("Unable to load json file: %s\nError: %s", filename, e.what());
|
||||
};
|
||||
}
|
||||
|
||||
void loadModelFromStream(std::istream& jsonStream)
|
||||
{
|
||||
int input_size;
|
||||
int input_skip;
|
||||
float input_gain;
|
||||
float output_gain;
|
||||
nlohmann::json model_json;
|
||||
|
||||
try {
|
||||
jsonStream >> model_json;
|
||||
|
||||
/* Understand which model type to load */
|
||||
input_size = model_json["in_shape"].back().get<int>();
|
||||
if (input_size > 1) { // MAX_INPUT_SIZE
|
||||
throw std::invalid_argument("Value for input_size not supported");
|
||||
}
|
||||
|
||||
if (model_json["in_skip"].is_number()) {
|
||||
input_skip = model_json["in_skip"].get<int>();
|
||||
if (input_skip > 1)
|
||||
throw std::invalid_argument("Values for in_skip > 1 are not supported");
|
||||
}
|
||||
else {
|
||||
input_skip = 0;
|
||||
}
|
||||
|
||||
if (model_json["in_gain"].is_number()) {
|
||||
input_gain = DB_CO(model_json["in_gain"].get<float>());
|
||||
}
|
||||
else {
|
||||
input_gain = 1.0f;
|
||||
}
|
||||
|
||||
if (model_json["out_gain"].is_number()) {
|
||||
output_gain = DB_CO(model_json["out_gain"].get<float>());
|
||||
}
|
||||
else {
|
||||
output_gain = 1.0f;
|
||||
}
|
||||
}
|
||||
catch (const std::exception& e) {
|
||||
d_stderr2("Unable to load json, error: %s", e.what());
|
||||
return;
|
||||
}
|
||||
|
||||
std::unique_ptr<DynamicModel> newmodel = std::make_unique<DynamicModel>();
|
||||
|
||||
try {
|
||||
if (! custom_model_creator (model_json, newmodel->variant))
|
||||
throw std::runtime_error ("Unable to identify a known model architecture!");
|
||||
|
||||
std::visit (
|
||||
[&model_json] (auto&& custom_model)
|
||||
{
|
||||
using ModelType = std::decay_t<decltype (custom_model)>;
|
||||
if constexpr (! std::is_same_v<ModelType, NullModel>)
|
||||
{
|
||||
custom_model.parseJson (model_json, true);
|
||||
custom_model.reset();
|
||||
}
|
||||
},
|
||||
newmodel->variant);
|
||||
}
|
||||
catch (const std::exception& e) {
|
||||
d_stderr2("Error loading model: %s", e.what());
|
||||
return;
|
||||
}
|
||||
|
||||
// save extra info
|
||||
newmodel->input_skip = input_skip != 0;
|
||||
newmodel->input_gain = input_gain;
|
||||
newmodel->output_gain = output_gain;
|
||||
|
||||
// Pre-buffer to avoid "clicks" during initialization
|
||||
float out[2048] = {};
|
||||
applyModel(newmodel.get(), out, ARRAY_SIZE(out));
|
||||
|
||||
// swap active model
|
||||
DynamicModel* const oldmodel = model;
|
||||
model = newmodel.release();
|
||||
|
||||
// if processing, wait for process cycle to complete
|
||||
while (oldmodel != nullptr && activeModel.load())
|
||||
d_msleep(1);
|
||||
|
||||
delete oldmodel;
|
||||
}
|
||||
|
||||
void process(const ProcessArgs& args) override
|
||||
{
|
||||
const float stime = args.sampleTime;
|
||||
const float inlevelv = DB_CO(params[PARAM_INPUT_LEVEL].getValue());
|
||||
const float outlevelv = DB_CO(params[PARAM_OUTPUT_LEVEL].getValue());
|
||||
|
||||
// High frequencies roll-off (lowpass)
|
||||
float sample = in_lpf.process(inputs[AUDIO_INPUT].getVoltage() * 0.1f) * inlevel.process(stime, inlevelv);
|
||||
|
||||
// run model
|
||||
if (model != nullptr)
|
||||
{
|
||||
activeModel.store(true);
|
||||
sample = applyModel(model, sample);
|
||||
activeModel.store(false);
|
||||
}
|
||||
|
||||
// DC blocker filter (highpass)
|
||||
outputs[AUDIO_OUTPUT].setVoltage(dc_blocker.process(sample) * outlevel.process(stime, outlevelv) * 10.f);
|
||||
}
|
||||
|
||||
void onSampleRateChange(const SampleRateChangeEvent& e) override
|
||||
{
|
||||
dc_blocker.setFc(35.0f / e.sampleRate);
|
||||
|
||||
in_lpf.setFc(MAP(66.216f, 0.0f, 100.0f, INLPF_MAX_CO, INLPF_MIN_CO));
|
||||
}
|
||||
|
||||
DISTRHO_DECLARE_NON_COPYABLE_WITH_LEAK_DETECTOR(AidaPluginModule)
|
||||
};
|
||||
|
||||
// --------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
#ifndef HEADLESS
|
||||
struct AidaModelListWidget : ImGuiWidget {
|
||||
AidaPluginModule* const module;
|
||||
|
||||
/*
|
||||
bool showError = false;
|
||||
String errorMessage;
|
||||
*/
|
||||
|
||||
struct ghcFile {
|
||||
std::string full, base;
|
||||
bool operator<(const ghcFile& other) const noexcept { return base < other.base; }
|
||||
};
|
||||
std::string currentDirectory;
|
||||
std::vector<ghcFile> currentFiles;
|
||||
size_t selectedFile = (size_t)-1;
|
||||
|
||||
AidaModelListWidget(AidaPluginModule* const m)
|
||||
: ImGuiWidget(),
|
||||
module(m)
|
||||
{
|
||||
if (module->fileChanged)
|
||||
reloadDir();
|
||||
}
|
||||
|
||||
void drawImGui() override
|
||||
{
|
||||
const float scaleFactor = getScaleFactor();
|
||||
|
||||
const int flags = ImGuiWindowFlags_NoSavedSettings
|
||||
| ImGuiWindowFlags_NoTitleBar
|
||||
| ImGuiWindowFlags_NoResize
|
||||
| ImGuiWindowFlags_NoCollapse
|
||||
| ImGuiWindowFlags_NoScrollbar
|
||||
| ImGuiWindowFlags_NoScrollWithMouse;
|
||||
|
||||
ImGui::SetNextWindowPos(ImVec2(0, 0));
|
||||
ImGui::SetNextWindowSize(ImVec2(box.size.x * scaleFactor, box.size.y * scaleFactor));
|
||||
|
||||
if (ImGui::Begin("Model File List", nullptr, ImGuiWindowFlags_NoTitleBar|ImGuiWindowFlags_NoResize))
|
||||
{
|
||||
/*
|
||||
if (showError)
|
||||
{
|
||||
showError = false;
|
||||
ImGui::OpenPopup("Audio File Error");
|
||||
}
|
||||
|
||||
if (ImGui::BeginPopupModal("Model File Error", nullptr, flags))
|
||||
{
|
||||
ImGui::TextWrapped("Failed to load model file, error was:\n%s", errorMessage.buffer());
|
||||
|
||||
ImGui::Separator();
|
||||
|
||||
if (ImGui::Button("Ok"))
|
||||
ImGui::CloseCurrentPopup();
|
||||
|
||||
ImGui::EndPopup();
|
||||
}
|
||||
else
|
||||
*/
|
||||
if (ImGui::BeginTable("modellist", 1, ImGuiTableFlags_NoSavedSettings))
|
||||
{
|
||||
for (size_t i=0, count=currentFiles.size(); i < count; ++i)
|
||||
{
|
||||
bool wasSelected = selectedFile == i;
|
||||
bool selected = wasSelected;
|
||||
ImGui::TableNextRow();
|
||||
ImGui::TableSetColumnIndex(0);
|
||||
ImGui::Selectable(currentFiles[i].base.c_str(), &selected);
|
||||
|
||||
if (selected && ! wasSelected)
|
||||
{
|
||||
selectedFile = i;
|
||||
module->currentFile = currentFiles[i].full;
|
||||
module->loadModelFromFile(currentFiles[i].full.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
ImGui::EndTable();
|
||||
}
|
||||
}
|
||||
|
||||
ImGui::End();
|
||||
}
|
||||
|
||||
void step() override
|
||||
{
|
||||
if (module->fileChanged)
|
||||
reloadDir();
|
||||
|
||||
ImGuiWidget::step();
|
||||
}
|
||||
|
||||
void reloadDir()
|
||||
{
|
||||
module->fileChanged = false;
|
||||
|
||||
currentFiles.clear();
|
||||
selectedFile = (size_t)-1;
|
||||
|
||||
static constexpr const char* const supportedExtensions[] = {
|
||||
".json"
|
||||
};
|
||||
|
||||
using namespace ghc::filesystem;
|
||||
const path currentFile = u8path(module->currentFile);
|
||||
currentDirectory = currentFile.parent_path().generic_u8string();
|
||||
|
||||
directory_iterator it;
|
||||
|
||||
try {
|
||||
it = directory_iterator(u8path(currentDirectory));
|
||||
} DISTRHO_SAFE_EXCEPTION_RETURN("Failed to open current directory",);
|
||||
|
||||
for (directory_iterator itb = begin(it), ite=end(it); itb != ite; ++itb)
|
||||
{
|
||||
if (! itb->is_regular_file())
|
||||
continue;
|
||||
const path filepath = itb->path();
|
||||
const path extension = filepath.extension();
|
||||
for (size_t i=0; i<ARRAY_SIZE(supportedExtensions); ++i)
|
||||
{
|
||||
if (extension.compare(supportedExtensions[i]) == 0)
|
||||
{
|
||||
currentFiles.push_back({ filepath.generic_u8string(), filepath.filename().generic_u8string() });
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::sort(currentFiles.begin(), currentFiles.end());
|
||||
|
||||
for (size_t index = 0; index < currentFiles.size(); ++index)
|
||||
{
|
||||
if (currentFiles[index].full.compare(currentFile) == 0)
|
||||
{
|
||||
selectedFile = index;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
struct AidaKnob : app::SvgKnob {
|
||||
AidaKnob()
|
||||
{
|
||||
minAngle = -0.76 * M_PI;
|
||||
maxAngle = 0.76 * M_PI;
|
||||
shadow->opacity = 0;
|
||||
setSvg(APP->window->loadSvg(asset::plugin(pluginInstance, "res/aida-x-knob.svg")));
|
||||
}
|
||||
};
|
||||
|
||||
struct AidaWidget : ModuleWidgetWithSideScrews<23> {
|
||||
static constexpr const float previewBoxHeight = 80.0f;
|
||||
static constexpr const float previewBoxBottom = 20.0f;
|
||||
static constexpr const float previewBoxRect[] = {8.0f,
|
||||
380.0f - previewBoxHeight - previewBoxBottom,
|
||||
15.0f * 23 - 16.0f,
|
||||
previewBoxHeight};
|
||||
static constexpr const float startY_list = startY - 2.0f;
|
||||
static constexpr const float fileListHeight = 380.0f - startY_list - previewBoxHeight - previewBoxBottom * 1.5f;
|
||||
static constexpr const float startY_preview = startY_list + fileListHeight;
|
||||
|
||||
AidaPluginModule* const module;
|
||||
|
||||
AidaWidget(AidaPluginModule* const m)
|
||||
: module(m)
|
||||
{
|
||||
setModule(module);
|
||||
setPanel(APP->window->loadSvg(asset::plugin(pluginInstance, "res/AIDA-X.svg")));
|
||||
|
||||
createAndAddScrews();
|
||||
|
||||
addInput(createInput<PJ301MPort>(Vec(startX_In, 25), module, 0));
|
||||
addOutput(createOutput<PJ301MPort>(Vec(startX_Out, 25), module, 0));
|
||||
|
||||
addChild(createParamCentered<AidaKnob>(Vec(box.size.x * 0.5f - 50, box.size.y - 60),
|
||||
module, AidaPluginModule::PARAM_INPUT_LEVEL));
|
||||
|
||||
addChild(createParamCentered<AidaKnob>(Vec(box.size.x * 0.5f + 50, box.size.y - 60),
|
||||
module, AidaPluginModule::PARAM_OUTPUT_LEVEL));
|
||||
|
||||
if (m != nullptr)
|
||||
{
|
||||
AidaModelListWidget* const listw = new AidaModelListWidget(m);
|
||||
listw->box.pos = Vec(0, startY_list);
|
||||
listw->box.size = Vec(box.size.x, fileListHeight);
|
||||
addChild(listw);
|
||||
}
|
||||
}
|
||||
|
||||
void draw(const DrawArgs& args) override
|
||||
{
|
||||
drawBackground(args.vg);
|
||||
drawOutputJacksArea(args.vg);
|
||||
|
||||
ModuleWidget::draw(args);
|
||||
}
|
||||
|
||||
void drawOutputJacksArea(NVGcontext* const vg)
|
||||
{
|
||||
nvgBeginPath(vg);
|
||||
nvgRoundedRect(vg, startX_Out - 2.5f, startY_list * 0.5f - padding * 0.5f, padding, padding, 4);
|
||||
nvgFillColor(vg, nvgRGB(0xd0, 0xd0, 0xd0));
|
||||
nvgFill(vg);
|
||||
}
|
||||
|
||||
void appendContextMenu(ui::Menu* const menu) override
|
||||
{
|
||||
menu->addChild(new ui::MenuSeparator);
|
||||
|
||||
struct LoadModelFileItem : MenuItem {
|
||||
AidaPluginModule* const module;
|
||||
|
||||
LoadModelFileItem(AidaPluginModule* const m)
|
||||
: module(m)
|
||||
{
|
||||
text = "Load model file...";
|
||||
}
|
||||
|
||||
void onAction(const event::Action&) override
|
||||
{
|
||||
AidaPluginModule* const module = this->module;
|
||||
async_dialog_filebrowser(false, nullptr, nullptr, text.c_str(), [module](char* path)
|
||||
{
|
||||
if (path == nullptr)
|
||||
return;
|
||||
|
||||
module->currentFile = path;
|
||||
module->fileChanged = true;
|
||||
module->loadModelFromFile(path);
|
||||
std::free(path);
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
menu->addChild(new LoadModelFileItem(module));
|
||||
}
|
||||
|
||||
DISTRHO_DECLARE_NON_COPYABLE_WITH_LEAK_DETECTOR(AidaWidget)
|
||||
};
|
||||
#else
|
||||
struct AidaWidget : ModuleWidget {
|
||||
AidaWidget(AidaPluginModule* const module) {
|
||||
setModule(module);
|
||||
|
||||
addInput(createInput<PJ301MPort>({}, module, 0));
|
||||
addOutput(createOutput<PJ301MPort>({}, module, 0));
|
||||
}
|
||||
};
|
||||
#endif
|
||||
|
||||
// --------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Model* modelAidaX = createModel<AidaPluginModule, AidaWidget>("AIDA-X");
|
||||
|
||||
// --------------------------------------------------------------------------------------------------------------------
|
||||
165
plugins/Cardinal/src/AIDA-X/Biquad.cpp
Normal file
165
plugins/Cardinal/src/AIDA-X/Biquad.cpp
Normal file
|
|
@ -0,0 +1,165 @@
|
|||
//
|
||||
// Biquad.cpp
|
||||
//
|
||||
// Created by Nigel Redmon on 11/24/12
|
||||
// EarLevel Engineering: earlevel.com
|
||||
// Copyright 2012 Nigel Redmon
|
||||
//
|
||||
// For a complete explanation of the Biquad code:
|
||||
// http://www.earlevel.com/main/2012/11/26/biquad-c-source-code/
|
||||
//
|
||||
// License:
|
||||
//
|
||||
// This source code is provided as is, without warranty.
|
||||
// You may copy and distribute verbatim copies of this document.
|
||||
// You may modify and use this source code to create binary code
|
||||
// for your own purposes, free or commercial.
|
||||
//
|
||||
|
||||
#include <math.h>
|
||||
#include "Biquad.h"
|
||||
|
||||
Biquad::Biquad() {
|
||||
type = bq_type_lowpass;
|
||||
a0 = 1.0;
|
||||
a1 = a2 = b1 = b2 = 0.0;
|
||||
Fc = 0.50;
|
||||
Q = 0.707;
|
||||
peakGain = 0.0;
|
||||
z1 = z2 = 0.0;
|
||||
}
|
||||
|
||||
Biquad::Biquad(int type, double Fc, double Q, double peakGainDB) {
|
||||
setBiquad(type, Fc, Q, peakGainDB);
|
||||
z1 = z2 = 0.0;
|
||||
}
|
||||
|
||||
Biquad::~Biquad() {
|
||||
}
|
||||
|
||||
void Biquad::setType(int type) {
|
||||
this->type = type;
|
||||
calcBiquad();
|
||||
}
|
||||
|
||||
void Biquad::setQ(double Q) {
|
||||
this->Q = Q;
|
||||
calcBiquad();
|
||||
}
|
||||
|
||||
void Biquad::setFc(double Fc) {
|
||||
this->Fc = Fc;
|
||||
calcBiquad();
|
||||
}
|
||||
|
||||
void Biquad::setPeakGain(double peakGainDB) {
|
||||
this->peakGain = peakGainDB;
|
||||
calcBiquad();
|
||||
}
|
||||
|
||||
void Biquad::setBiquad(int type, double Fc, double Q, double peakGainDB) {
|
||||
this->type = type;
|
||||
this->Q = Q;
|
||||
this->Fc = Fc;
|
||||
setPeakGain(peakGainDB);
|
||||
}
|
||||
|
||||
void Biquad::calcBiquad(void) {
|
||||
double norm;
|
||||
double V = pow(10, fabs(peakGain) / 20.0);
|
||||
double K = tan(M_PI * Fc);
|
||||
switch (this->type) {
|
||||
case bq_type_lowpass:
|
||||
norm = 1 / (1 + K / Q + K * K);
|
||||
a0 = K * K * norm;
|
||||
a1 = 2 * a0;
|
||||
a2 = a0;
|
||||
b1 = 2 * (K * K - 1) * norm;
|
||||
b2 = (1 - K / Q + K * K) * norm;
|
||||
break;
|
||||
|
||||
case bq_type_highpass:
|
||||
norm = 1 / (1 + K / Q + K * K);
|
||||
a0 = 1 * norm;
|
||||
a1 = -2 * a0;
|
||||
a2 = a0;
|
||||
b1 = 2 * (K * K - 1) * norm;
|
||||
b2 = (1 - K / Q + K * K) * norm;
|
||||
break;
|
||||
|
||||
case bq_type_bandpass:
|
||||
norm = 1 / (1 + K / Q + K * K);
|
||||
a0 = K / Q * norm;
|
||||
a1 = 0;
|
||||
a2 = -a0;
|
||||
b1 = 2 * (K * K - 1) * norm;
|
||||
b2 = (1 - K / Q + K * K) * norm;
|
||||
break;
|
||||
|
||||
case bq_type_notch:
|
||||
norm = 1 / (1 + K / Q + K * K);
|
||||
a0 = (1 + K * K) * norm;
|
||||
a1 = 2 * (K * K - 1) * norm;
|
||||
a2 = a0;
|
||||
b1 = a1;
|
||||
b2 = (1 - K / Q + K * K) * norm;
|
||||
break;
|
||||
|
||||
case bq_type_peak:
|
||||
if (peakGain >= 0) { // boost
|
||||
norm = 1 / (1 + 1/Q * K + K * K);
|
||||
a0 = (1 + V/Q * K + K * K) * norm;
|
||||
a1 = 2 * (K * K - 1) * norm;
|
||||
a2 = (1 - V/Q * K + K * K) * norm;
|
||||
b1 = a1;
|
||||
b2 = (1 - 1/Q * K + K * K) * norm;
|
||||
}
|
||||
else { // cut
|
||||
norm = 1 / (1 + V/Q * K + K * K);
|
||||
a0 = (1 + 1/Q * K + K * K) * norm;
|
||||
a1 = 2 * (K * K - 1) * norm;
|
||||
a2 = (1 - 1/Q * K + K * K) * norm;
|
||||
b1 = a1;
|
||||
b2 = (1 - V/Q * K + K * K) * norm;
|
||||
}
|
||||
break;
|
||||
case bq_type_lowshelf:
|
||||
if (peakGain >= 0) { // boost
|
||||
norm = 1 / (1 + sqrt(2) * K + K * K);
|
||||
a0 = (1 + sqrt(2*V) * K + V * K * K) * norm;
|
||||
a1 = 2 * (V * K * K - 1) * norm;
|
||||
a2 = (1 - sqrt(2*V) * K + V * K * K) * norm;
|
||||
b1 = 2 * (K * K - 1) * norm;
|
||||
b2 = (1 - sqrt(2) * K + K * K) * norm;
|
||||
}
|
||||
else { // cut
|
||||
norm = 1 / (1 + sqrt(2*V) * K + V * K * K);
|
||||
a0 = (1 + sqrt(2) * K + K * K) * norm;
|
||||
a1 = 2 * (K * K - 1) * norm;
|
||||
a2 = (1 - sqrt(2) * K + K * K) * norm;
|
||||
b1 = 2 * (V * K * K - 1) * norm;
|
||||
b2 = (1 - sqrt(2*V) * K + V * K * K) * norm;
|
||||
}
|
||||
break;
|
||||
case bq_type_highshelf:
|
||||
if (peakGain >= 0) { // boost
|
||||
norm = 1 / (1 + sqrt(2) * K + K * K);
|
||||
a0 = (V + sqrt(2*V) * K + K * K) * norm;
|
||||
a1 = 2 * (K * K - V) * norm;
|
||||
a2 = (V - sqrt(2*V) * K + K * K) * norm;
|
||||
b1 = 2 * (K * K - 1) * norm;
|
||||
b2 = (1 - sqrt(2) * K + K * K) * norm;
|
||||
}
|
||||
else { // cut
|
||||
norm = 1 / (V + sqrt(2*V) * K + K * K);
|
||||
a0 = (1 + sqrt(2) * K + K * K) * norm;
|
||||
a1 = 2 * (K * K - 1) * norm;
|
||||
a2 = (1 - sqrt(2) * K + K * K) * norm;
|
||||
b1 = 2 * (K * K - V) * norm;
|
||||
b2 = (V - sqrt(2*V) * K + K * K) * norm;
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
return;
|
||||
}
|
||||
60
plugins/Cardinal/src/AIDA-X/Biquad.h
Normal file
60
plugins/Cardinal/src/AIDA-X/Biquad.h
Normal file
|
|
@ -0,0 +1,60 @@
|
|||
//
|
||||
// Biquad.h
|
||||
//
|
||||
// Created by Nigel Redmon on 11/24/12
|
||||
// EarLevel Engineering: earlevel.com
|
||||
// Copyright 2012 Nigel Redmon
|
||||
//
|
||||
// For a complete explanation of the Biquad code:
|
||||
// http://www.earlevel.com/main/2012/11/26/biquad-c-source-code/
|
||||
//
|
||||
// License:
|
||||
//
|
||||
// This source code is provided as is, without warranty.
|
||||
// You may copy and distribute verbatim copies of this document.
|
||||
// You may modify and use this source code to create binary code
|
||||
// for your own purposes, free or commercial.
|
||||
//
|
||||
|
||||
#ifndef Biquad_h
|
||||
#define Biquad_h
|
||||
|
||||
enum {
|
||||
bq_type_lowpass = 0,
|
||||
bq_type_highpass,
|
||||
bq_type_bandpass,
|
||||
bq_type_notch,
|
||||
bq_type_peak,
|
||||
bq_type_lowshelf,
|
||||
bq_type_highshelf
|
||||
};
|
||||
|
||||
class Biquad {
|
||||
public:
|
||||
Biquad();
|
||||
Biquad(int type, double Fc, double Q, double peakGainDB);
|
||||
~Biquad();
|
||||
void setType(int type);
|
||||
void setQ(double Q);
|
||||
void setFc(double Fc);
|
||||
void setPeakGain(double peakGainDB);
|
||||
void setBiquad(int type, double Fc, double Q, double peakGainDB);
|
||||
float process(float in);
|
||||
|
||||
protected:
|
||||
void calcBiquad(void);
|
||||
|
||||
int type;
|
||||
double a0, a1, a2, b1, b2;
|
||||
double Fc, Q, peakGain;
|
||||
double z1, z2;
|
||||
};
|
||||
|
||||
inline float Biquad::process(float in) {
|
||||
double out = in * a0 + z1;
|
||||
z1 = in * a1 + z2 - b1 * out;
|
||||
z2 = in * a2 - b2 * out;
|
||||
return out;
|
||||
}
|
||||
|
||||
#endif // Biquad_h
|
||||
1
plugins/Cardinal/src/AIDA-X/RTNeural
Submodule
1
plugins/Cardinal/src/AIDA-X/RTNeural
Submodule
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 74e9d354937346f31858e976a2eefc1c25cdcccd
|
||||
683
plugins/Cardinal/src/AIDA-X/model_variant.hpp
Normal file
683
plugins/Cardinal/src/AIDA-X/model_variant.hpp
Normal file
|
|
@ -0,0 +1,683 @@
|
|||
#include <variant>
|
||||
#include <RTNeural/RTNeural.h>
|
||||
|
||||
#define MAX_INPUT_SIZE 3
|
||||
struct NullModel { static constexpr int input_size = 0; static constexpr int output_size = 0; };
|
||||
using ModelType_GRU_8_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 8>, RTNeural::DenseT<float, 8, 1>>;
|
||||
using ModelType_GRU_8_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 8>, RTNeural::DenseT<float, 8, 1>>;
|
||||
using ModelType_GRU_8_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 8>, RTNeural::DenseT<float, 8, 1>>;
|
||||
using ModelType_GRU_12_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 12>, RTNeural::DenseT<float, 12, 1>>;
|
||||
using ModelType_GRU_12_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 12>, RTNeural::DenseT<float, 12, 1>>;
|
||||
using ModelType_GRU_12_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 12>, RTNeural::DenseT<float, 12, 1>>;
|
||||
using ModelType_GRU_16_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 16>, RTNeural::DenseT<float, 16, 1>>;
|
||||
using ModelType_GRU_16_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 16>, RTNeural::DenseT<float, 16, 1>>;
|
||||
using ModelType_GRU_16_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 16>, RTNeural::DenseT<float, 16, 1>>;
|
||||
using ModelType_GRU_20_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 20>, RTNeural::DenseT<float, 20, 1>>;
|
||||
using ModelType_GRU_20_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 20>, RTNeural::DenseT<float, 20, 1>>;
|
||||
using ModelType_GRU_20_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 20>, RTNeural::DenseT<float, 20, 1>>;
|
||||
using ModelType_GRU_32_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 32>, RTNeural::DenseT<float, 32, 1>>;
|
||||
using ModelType_GRU_32_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 32>, RTNeural::DenseT<float, 32, 1>>;
|
||||
using ModelType_GRU_32_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 32>, RTNeural::DenseT<float, 32, 1>>;
|
||||
using ModelType_GRU_40_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 40>, RTNeural::DenseT<float, 40, 1>>;
|
||||
using ModelType_GRU_40_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 40>, RTNeural::DenseT<float, 40, 1>>;
|
||||
using ModelType_GRU_40_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 40>, RTNeural::DenseT<float, 40, 1>>;
|
||||
using ModelType_GRU_64_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 64>, RTNeural::DenseT<float, 64, 1>>;
|
||||
using ModelType_GRU_64_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 64>, RTNeural::DenseT<float, 64, 1>>;
|
||||
using ModelType_GRU_64_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 64>, RTNeural::DenseT<float, 64, 1>>;
|
||||
using ModelType_LSTM_8_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 8>, RTNeural::DenseT<float, 8, 1>>;
|
||||
using ModelType_LSTM_8_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 8>, RTNeural::DenseT<float, 8, 1>>;
|
||||
using ModelType_LSTM_8_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 8>, RTNeural::DenseT<float, 8, 1>>;
|
||||
using ModelType_LSTM_12_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 12>, RTNeural::DenseT<float, 12, 1>>;
|
||||
using ModelType_LSTM_12_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 12>, RTNeural::DenseT<float, 12, 1>>;
|
||||
using ModelType_LSTM_12_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 12>, RTNeural::DenseT<float, 12, 1>>;
|
||||
using ModelType_LSTM_16_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 16>, RTNeural::DenseT<float, 16, 1>>;
|
||||
using ModelType_LSTM_16_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 16>, RTNeural::DenseT<float, 16, 1>>;
|
||||
using ModelType_LSTM_16_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 16>, RTNeural::DenseT<float, 16, 1>>;
|
||||
using ModelType_LSTM_20_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 20>, RTNeural::DenseT<float, 20, 1>>;
|
||||
using ModelType_LSTM_20_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 20>, RTNeural::DenseT<float, 20, 1>>;
|
||||
using ModelType_LSTM_20_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 20>, RTNeural::DenseT<float, 20, 1>>;
|
||||
using ModelType_LSTM_32_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 32>, RTNeural::DenseT<float, 32, 1>>;
|
||||
using ModelType_LSTM_32_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 32>, RTNeural::DenseT<float, 32, 1>>;
|
||||
using ModelType_LSTM_32_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 32>, RTNeural::DenseT<float, 32, 1>>;
|
||||
using ModelType_LSTM_40_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 40>, RTNeural::DenseT<float, 40, 1>>;
|
||||
using ModelType_LSTM_40_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 40>, RTNeural::DenseT<float, 40, 1>>;
|
||||
using ModelType_LSTM_40_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 40>, RTNeural::DenseT<float, 40, 1>>;
|
||||
using ModelType_LSTM_64_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 64>, RTNeural::DenseT<float, 64, 1>>;
|
||||
using ModelType_LSTM_64_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 64>, RTNeural::DenseT<float, 64, 1>>;
|
||||
using ModelType_LSTM_64_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 64>, RTNeural::DenseT<float, 64, 1>>;
|
||||
using ModelVariantType = std::variant<NullModel,ModelType_GRU_8_1,ModelType_GRU_8_2,ModelType_GRU_8_3,ModelType_GRU_12_1,ModelType_GRU_12_2,ModelType_GRU_12_3,ModelType_GRU_16_1,ModelType_GRU_16_2,ModelType_GRU_16_3,ModelType_GRU_20_1,ModelType_GRU_20_2,ModelType_GRU_20_3,ModelType_GRU_32_1,ModelType_GRU_32_2,ModelType_GRU_32_3,ModelType_GRU_40_1,ModelType_GRU_40_2,ModelType_GRU_40_3,ModelType_GRU_64_1,ModelType_GRU_64_2,ModelType_GRU_64_3,ModelType_LSTM_8_1,ModelType_LSTM_8_2,ModelType_LSTM_8_3,ModelType_LSTM_12_1,ModelType_LSTM_12_2,ModelType_LSTM_12_3,ModelType_LSTM_16_1,ModelType_LSTM_16_2,ModelType_LSTM_16_3,ModelType_LSTM_20_1,ModelType_LSTM_20_2,ModelType_LSTM_20_3,ModelType_LSTM_32_1,ModelType_LSTM_32_2,ModelType_LSTM_32_3,ModelType_LSTM_40_1,ModelType_LSTM_40_2,ModelType_LSTM_40_3,ModelType_LSTM_64_1,ModelType_LSTM_64_2,ModelType_LSTM_64_3>;
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_8_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 8;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_8_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 8;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_8_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 8;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_12_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 12;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_12_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 12;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_12_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 12;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_16_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 16;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_16_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 16;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_16_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 16;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_20_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 20;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_20_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 20;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_20_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 20;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_32_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 32;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_32_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 32;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_32_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 32;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_40_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 40;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_40_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 40;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_40_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 40;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_64_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 64;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_64_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 64;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_GRU_64_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "gru";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 64;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_8_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 8;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_8_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 8;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_8_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 8;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_12_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 12;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_12_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 12;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_12_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 12;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_16_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 16;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_16_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 16;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_16_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 16;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_20_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 20;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_20_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 20;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_20_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 20;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_32_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 32;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_32_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 32;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_32_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 32;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_40_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 40;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_40_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 40;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_40_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 40;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_64_1 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 64;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 1;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_64_2 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 64;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 2;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool is_model_type_ModelType_LSTM_64_3 (const nlohmann::json& model_json) {
|
||||
const auto json_layers = model_json.at ("layers");
|
||||
const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
|
||||
const auto is_layer_type_correct = rnn_layer_type == "lstm";
|
||||
const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
|
||||
const auto is_hidden_size_correct = hidden_size == 64;
|
||||
const auto input_size = model_json.at ("in_shape").back().get<int>();
|
||||
const auto is_input_size_correct = input_size == 3;
|
||||
return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
|
||||
}
|
||||
|
||||
inline bool custom_model_creator (const nlohmann::json& model_json, ModelVariantType& model) {
|
||||
if (is_model_type_ModelType_GRU_8_1 (model_json)) {
|
||||
model.emplace<ModelType_GRU_8_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_8_2 (model_json)) {
|
||||
model.emplace<ModelType_GRU_8_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_8_3 (model_json)) {
|
||||
model.emplace<ModelType_GRU_8_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_12_1 (model_json)) {
|
||||
model.emplace<ModelType_GRU_12_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_12_2 (model_json)) {
|
||||
model.emplace<ModelType_GRU_12_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_12_3 (model_json)) {
|
||||
model.emplace<ModelType_GRU_12_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_16_1 (model_json)) {
|
||||
model.emplace<ModelType_GRU_16_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_16_2 (model_json)) {
|
||||
model.emplace<ModelType_GRU_16_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_16_3 (model_json)) {
|
||||
model.emplace<ModelType_GRU_16_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_20_1 (model_json)) {
|
||||
model.emplace<ModelType_GRU_20_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_20_2 (model_json)) {
|
||||
model.emplace<ModelType_GRU_20_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_20_3 (model_json)) {
|
||||
model.emplace<ModelType_GRU_20_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_32_1 (model_json)) {
|
||||
model.emplace<ModelType_GRU_32_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_32_2 (model_json)) {
|
||||
model.emplace<ModelType_GRU_32_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_32_3 (model_json)) {
|
||||
model.emplace<ModelType_GRU_32_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_40_1 (model_json)) {
|
||||
model.emplace<ModelType_GRU_40_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_40_2 (model_json)) {
|
||||
model.emplace<ModelType_GRU_40_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_40_3 (model_json)) {
|
||||
model.emplace<ModelType_GRU_40_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_64_1 (model_json)) {
|
||||
model.emplace<ModelType_GRU_64_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_64_2 (model_json)) {
|
||||
model.emplace<ModelType_GRU_64_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_GRU_64_3 (model_json)) {
|
||||
model.emplace<ModelType_GRU_64_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_8_1 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_8_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_8_2 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_8_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_8_3 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_8_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_12_1 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_12_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_12_2 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_12_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_12_3 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_12_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_16_1 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_16_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_16_2 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_16_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_16_3 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_16_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_20_1 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_20_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_20_2 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_20_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_20_3 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_20_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_32_1 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_32_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_32_2 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_32_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_32_3 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_32_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_40_1 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_40_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_40_2 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_40_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_40_3 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_40_3>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_64_1 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_64_1>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_64_2 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_64_2>();
|
||||
return true;
|
||||
}
|
||||
else if (is_model_type_ModelType_LSTM_64_3 (model_json)) {
|
||||
model.emplace<ModelType_LSTM_64_3>();
|
||||
return true;
|
||||
}
|
||||
model.emplace<NullModel>();
|
||||
return false;
|
||||
}
|
||||
|
|
@ -28,6 +28,7 @@ using namespace rack;
|
|||
|
||||
extern Plugin* pluginInstance;
|
||||
|
||||
extern Model* modelAidaX;
|
||||
extern Model* modelAudioFile;
|
||||
extern Model* modelAudioToCVPitch;
|
||||
extern Model* modelCarla;
|
||||
|
|
|
|||
|
|
@ -223,6 +223,7 @@ MINIPLUGIN_FILES = plugins-mini.cpp
|
|||
# --------------------------------------------------------------
|
||||
# Cardinal (built-in)
|
||||
|
||||
PLUGIN_FILES += Cardinal/src/AIDA-X.cpp
|
||||
PLUGIN_FILES += Cardinal/src/Blank.cpp
|
||||
PLUGIN_FILES += Cardinal/src/ExpanderInputMIDI.cpp
|
||||
PLUGIN_FILES += Cardinal/src/ExpanderOutputMIDI.cpp
|
||||
|
|
@ -1306,6 +1307,20 @@ ifeq ($(WASM),true)
|
|||
CARLA_FLAGS += -DDISTRHO_RUNNER_INDIRECT_WASM_CALLS
|
||||
endif
|
||||
|
||||
# --------------------------------------------------------------
|
||||
# RTNeural flags, used in AIDA-X
|
||||
|
||||
RTNEURAL_FLAGS = -std=gnu++17
|
||||
RTNEURAL_FLAGS += -DRTNEURAL_DEFAULT_ALIGNMENT=16
|
||||
RTNEURAL_FLAGS += -DRTNEURAL_USE_EIGEN=1
|
||||
|
||||
ifeq ($(WASM),true)
|
||||
RTNEURAL_FLAGS += -DEIGEN_DONT_VECTORIZE=1
|
||||
endif
|
||||
|
||||
RTNEURAL_FLAGS += -ICardinal/src/AIDA-X/RTNeural
|
||||
RTNEURAL_FLAGS += -ICardinal/src/AIDA-X/RTNeural/modules/Eigen
|
||||
|
||||
# --------------------------------------------------------------
|
||||
# Build targets
|
||||
|
||||
|
|
@ -1975,6 +1990,19 @@ $(BUILD_DIR)/Cardinal/%.cpp.o: Cardinal/%.cpp
|
|||
-Dstbrp_rect=stbrp_rect_cardinal \
|
||||
$(CARLA_FLAGS)
|
||||
|
||||
$(BUILD_DIR)/Cardinal/src/AIDA-X.cpp.o: Cardinal/src/AIDA-X.cpp
|
||||
-@mkdir -p "$(shell dirname $(BUILD_DIR)/$<)"
|
||||
@echo "Compiling $<"
|
||||
$(SILENT)$(CXX) $< $(BUILD_CXX_FLAGS) -c -o $@ \
|
||||
-DpluginInstance=pluginInstance__Cardinal \
|
||||
-Dstbrp_context=stbrp_context_cardinal \
|
||||
-Dstbrp_coord=stbrp_coord_cardinal \
|
||||
-Dstbtt_fontinfo=stbtt_fontinfo_cardinal \
|
||||
-Dstbrp_node=stbrp_node_cardinal \
|
||||
-Dstbrp_rect=stbrp_rect_cardinal \
|
||||
$(RTNEURAL_FLAGS) \
|
||||
$(CARLA_FLAGS)
|
||||
|
||||
$(BUILD_DIR)/21kHz/%.cpp.o: 21kHz/%.cpp
|
||||
-@mkdir -p "$(shell dirname $(BUILD_DIR)/$<)"
|
||||
@echo "Compiling $<"
|
||||
|
|
|
|||
|
|
@ -212,6 +212,7 @@ static void initStatic__Cardinal()
|
|||
/*
|
||||
#endif
|
||||
*/
|
||||
spl.removeModule("AIDA-X");
|
||||
spl.removeModule("AudioFile");
|
||||
spl.removeModule("Blank");
|
||||
spl.removeModule("Carla");
|
||||
|
|
|
|||
|
|
@ -972,6 +972,7 @@ static void initStatic__Cardinal()
|
|||
const StaticPluginLoader spl(p, "Cardinal");
|
||||
if (spl.ok())
|
||||
{
|
||||
p->addModel(modelAidaX);
|
||||
p->addModel(modelCardinalBlank);
|
||||
p->addModel(modelExpanderInputMIDI);
|
||||
p->addModel(modelExpanderOutputMIDI);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue